real time human violence recognition and localization for indoor video using deep learning

Abstract

With storming growth in technology, there is an explosion in surveillance systems deployment on public as well as private locations such as malls, hospitals, banks, society etc. Rising surveillance systems enable better governance and control in the surrounding environment when it comes to security, safety, risk management, prevention of adversaries etc. This revolution has sparked the interest of researchers in the area of computer vision with its potential real-world applications. Under the narrow field of view, it has opened up new opportunities to better understand environmental dynamics through human behavior understanding, its causes, correlations with surrounding environment, extracting previously unknown yet potentially useful hidden patterns which can collectively elevate safety and security of humankind. As a sub domain of behavior understanding, detection and tracking of abnormal or to be precise violent events detection and monitoring is still an open challenge in the area of research. Contextual definition of human violent action recognition can be termed as any event that poses threat to human life safety. However, continuous manual monitoring by security professionals is highly stressful, inadequate, prone to human errors and inefficient. Hence, it is important if human intervention in such tasks can be minimized as much as possible by the means of automation. Besides, the evolution of social media has posed another challenge as video footage is shared globally and becomes viral that is not only to detect violent events, but it creates necessity to also hide or blur out sensitive graphic contents on demand as its collective psychological impacts on viewers which can breed communal, political riots. Due to the subjectivity of sensitive information, violent action detection and localization is still a less explored research area. With potential application in moderating spread of online sensitive content, current research is still limited when it comes to multi-class violence detection and localizati